An inflammation-based model for identifying severe acute pancreatitis: a single-center retrospective study

Background and aims Severe acute pancreatitis (SAP) is potentially lethal. Considering the role of inflammation in the progression of acute pancreatitis (AP), this study aims to develop a model based on inflammatory indexes for identifying the presence of SAP. Methods Overall, 253 patients with AP who were consecutively admitted between July 2018 and November 2020 were screened, of whom 60 had SAP. Systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), neutrophil-to-platelet ratio (NPR), systemic inflammation response index (SIRI), platelet-to-albumin ratio (PAR), C-reactive protein-to-albumin ratio (CAR), C-reactive protein-to-lymphocyte ratio (CLR), and triglyceride glucose (TyG) index were calculated. Multivariate logistic regression analyses were performed to identify independent risk factors of SAP. Then, inflammation-based models were established. Receiver operating characteristics (ROC) curve analyses were performed. Area under ROC curve (AUROC) was calculated. Results Diabetes mellitus, fatty liver, high white blood cell count (WBC), C-reactive protein (CRP), red blood cell distribution width (RDW), procalcitonin (PCT), SII, NLR, NPR, CAR, CLR, and TyG index, and a low LMR were significantly associated with SAP. Considering the collinearity among these variables, 10 multivariate logistic regression analyses were separately performed. Finally, four independent inflammation-based models were established. Of them, the best one, which was calculated as follows: 1.204*fatty liver (yes = 1; no = 0) + 0.419*PCT + 0.005*CLR - 2.629, had an AUROC of 0.795 with a specificity of 73.4% and a sensitivity of 71.7%. Conclusion The inflammation-based model consisting of fatty liver, PCT, and CLR has a good diagnostic performance for SAP. Supplementary Information The online version contains supplementary material available at 10.1186/s12876-024-03148-4.


Introduction
Acute pancreatitis (AP) is an inflammatory disorder triggered by pancreatic enzyme infiltration [1] with an incidence of 34 cases per 100,000 person-years around the world [2,3].Its main clinical presentation includes severe abdominal pain with or without nausea, vomiting, and fever [4].It is mostly mild and may resolve within a few days [5].However, about 20% of the patients progress to severe acute pancreatitis (SAP), which is characterized by systemic inflammatory response syndrome (SIRS) with single or multiple organ failure [5,6], with a mortality of up to 50% [7,8].Identification of the patients who are at a high risk of developing SAP is necessary for predicting their outcomes and guiding the treatment strategy [9].
Inflammation is critical for the development and progression of SAP [10].During the course of SAP, excessive inflammatory mediators are released, inducing inflammatory cascade reaction, ultimately causing bacterial translocation and secondary injuries of distant tissues and organs [11,12].Thus, it seems to be reasonable that inflammatory indexes, such as neutrophil to lymphocyte ratio (NLR) and lymphocyte to monocyte ratio (LMR), can predict the probability of SAP [13,14].But their performance was very limited, probably because only a single index was employed in previous studies.Tanoğlu et al. also suggested that NLR alone may not truly reflect the severity of AP due to the possible influencing factors, such as other diseases [15].Subsequently, some researchers have attempted to combine various inflammatory indexes for the prediction of SAP.Kaplan et al. found a similar predictive performance of the platelet to lymphocyte ratio (PLR)-NLR combination with other scoring systems for determining the prognosis of AP patients.But there is collinearity between PLR and NLR [16].Zhu et al. also reported a good predictive value of a combination of NLR, procalcitonin (PCT), and modified computerized tomography severity index (MCTSI) for infected pancreatic necrosis, a form of SAP [17].But it requires the results of imaging examinations except for inflammatory indexes.Herein, we aimed to develop a model for identifying SAP by combining various inflammatory indexes.

Study design
The retrospective study was performed according to the Declaration of Helsinki and was approved by the Medical Ethical Committee of the General Hospital of Northern Theater Command (approval number: Y2023-120).Written informed consent was waived by the Medical Ethical Committee of the General Hospital of Northern Theater Command due to its retrospective nature.We reviewed the medical records of all patients who were diagnosed with AP and consecutively admitted to the General Hospital of Northern Theater Command between July 4, 2018 and November 20, 2020 from the Information Section of Medical Security Center.The exclusion criteria were as follows: (1) age < 18 or > 80 years; (2) medical records cannot be reviewed in detail; (3) the interval between onset of symptoms and admission was more than seven days; (4) the hospital stay was less than 5 days; (5) co-existing severe trauma or pregnancy; (6) co-existing chronic pancreatitis; and (7) co-existing viral infection or rheumatic diseases.

Group and definition
In the present study, the patients were classified into SAP and N-SAP group according to the revised Atlanta criteria and Ranson and BISAP scoring systems.According to the revised Atlanta criteria, AP is classified as follows [28]: (1) mild acute pancreatitis (MAP), which is defined if patients have neither local complications nor organ failure; (2) moderately acute pancreatitis (MSAP), which is defined if patients have transient organ failure (< 48 h) and/or local complications; (3) SAP, which is defined if patients have persistent organ failure (≥ 48 h) with or without local complications.Ranson score is calculated based on 11 variables: age > 55 years, WBC > 16,000/μL, lactate dehydroge-nase> 350 U/L, aspartate transaminase> 250 U/L, and blood glucose> 200 mg/dL at admission, and fall in hematocrit> 10%, increase in blood urea nitrogen (BUN) > 5 mg/dL, calcium< 8 mg/dL, PaO 2 < 60 mmHg, base deficit> 4 mEq/L, and fluid loss> 6 L within 48 h after admission [29].BISAP score is calculated based on five variables: BUN> 25 mg/dL, impaired mental status, SIRS, age > 60 years, and radiographic evidence of pleural effusion within the first 24 hours after admission [30].Ranson or BISAP score ≥ 3 is defined as SAP; otherwise, N-SAP is considered.Because not all of our patients had the data at 24 h or 48 h, we selected the value reflecting the most severe clinical condition during their hospitalizations.

Statistical analyses
Continuous variables were presented as mean ± standard deviation (SD) and median with range.If the variables followed normal distribution, their differences between groups would be evaluated by independent sample T-test; otherwise, their differences between groups would be evaluated by Mann-Whitney U test.Categorical variables were presented as frequency with percentage.Differences between groups were evaluated by Chi-squared test or Fisher's exact test.Statistically significant factors in the univariate logistic regression analyses were included in the multivariate logistic regression analyses.Multivariate logistic regression models were established after eliminating the factors with collinearity.The discrimination of the models was evaluated and compared by calculating the area under the receiver operating characteristic curve (AUROC).The concordance index (c-index) was calculated, and the calibration curve was plotted by

Characteristics of patients
Initially, 336 AP patients were screened.Finally, 253 patients were included, of whom 60 and 193 were assigned to SAP and N-SAP group, respectively (Fig. 1).Baseline characteristics of the patients were shown in Table 1.
The mean age was 45.98 ± 0.89 years, and 64.0% of the patients were male.The mean length of hospital stay was 12.02 ± 0.35 days, the mean interval between onset of symptom and admission was 1.50 ± 0.11 days, and the mean hospitalization expense was 27,160.88± 1279.54 yuan.

Difference between SAP and N-SAP groups
Gender, diabetes mellitus, and fatty liver were significantly different between the two groups.WBC, CRP, RDW, PDW, PCT, SII, NLR, PLR, NPR, SIRI, PAR, CAR, CLR, TyG index, length of hospital stay, and hospitalization expense were significantly higher in the SAP group than the N-SAP group.LMR was significantly lower in the SAP group than the N-SAP group.Age, history of smoking, drinking, and AP, hypertriglyceridemia, hypertension, and interval between onset of symptom and admission were statistically similar between them (Table 2).

Inflammatory index models
Univariate logistic regression analyses demonstrated that fatty liver, high WBC, CRP, RDW, PCT, SII, NLR, NPR, CAR, CLR, and TyG index, and a low LMR were significantly associated with the presence of SAP (Table 3).

Discussion
In the present study, four inflammation-based models for identifying the presence of SAP have been established, of which one, consisting of fatty liver, PCT, and CLR, has  a superior diagnostic performance with an accuracy of 79.5%.Notably, the three components are easily obtained through routine blood tests, allowing to assess the severity of AP rapidly.CLR derives from CRP and lymphocyte.It was widely used to predict the prognosis in many diseases, such as oral cavity squamous cell carcinoma, pancreatic cancer, and colorectal cancer [26,31,32].In our patients, CLR was positively associated with the risk of SAP.The possible reasons are as follows.First, CRP, an acute-phase protein, elevates dramatically during inflammation [33].This is because that transcriptional induction of the CRP gene mainly occurs in response to an increase of inflammatory cytokines, especially IL-6 [34].In SAP patients, IL-6 was excessively released [35,36].Besides, CRP is deposited at inflammatory sites and amplifies a pro-inflammatory response by a positive feedback loop [37].Taken together, CRP rises in SAP patients.Second, lymphocyte counts are significantly reduced in SAP [38].This may be due to the effects of endotoxins released from bacteria and cytokines on T lymphocyte reduction [39] and cytokines released from monocytes or endothelial cells on the apoptosis of peripheral lymphocytes [40].
PCT, a protein of 116 amino acids, is coded for by the calcitonin I (CALC-I) gene [41].Serum PCT level is elevated in infectious diseases or conditions [42].PCT was first found to be associated with the severity of infection in 1993 [43].Later, it was demonstrated that PCT was a good predictor of short-term survival in patients with sepsis and pneumonia [44,45].Besides, high PCT level could predict the probability of SAP [46][47][48].Similarly, our study found that PCT was an independent risk factor of SAP.This finding may be attributed to the fact that an increase of cytokines in SAP patients causes endotoxemia, inducing CALC-I expression in pancreas [49].
Previous studies also demonstrated the importance of other inflammatory indexes and prognostic scores for predicting the AP severity.Jain et al. reported that inflammatory indexes, including NLR, LMR, RDW, and PNI, were comparable to gold standard scoring systems for predicting the severity and mortality of AP [50].Besides, CAR also has good predictive value in AP severity.It derives from CRP and albumin, which can be calculated rapidly and easily.Kiyak et al. compared CAR with traditional scores, and showed that CAR values were positively correlated with Balthazar score, and its AUC was higher than that of NLR and PLR in mortality prediction of AP [51].However, their predictive values have not been confirmed in our study.
There were some limitations in our study.First, due to the retrospective nature of this study, some data could not be sufficiently extracted.Thus, we had to define SAP by meeting any of the three following criteria: the revised Atlanta criteria, BISAP score, or Ranson score.Besides, the extreme data obtained during hospitalizations, but not the data at baseline, was used to establish the model.Second, it should be noted that the interval between the onset of symptoms and laboratory assessment was different among our patients, but it could not be controlled due to the retrospective nature of our study.Third, this study was only performed at a single center.Therefore, the findings should be externally validated at other affiliations.Forth, only a relatively small number of AP patients were included.Thus, the findings should be validated in large-scale studies.

Conclusion
Our study suggested an association of SAP with higher levels of SII, NLR, NPR, CAR, CLR, RDW, PDW, PCT, and TyG index, and a lower level of LMR.We developed an inflammation-based model comprising fatty liver, PCT, and CLR for identifying the presence of SAP with a good diagnostic ability.In future, multi-center studies should be conducted to validate these findings.

Fig. 1 A
Fig. 1 A flow chart of patients' selection

Table 1
Baseline characteristics of the AP patientsAbbreviations: SAP severe acute pancreatitis, AP acute pancreatitis, No. Pts number of patients, CRP C-reactive protein, RDW red blood cell distribution width, PDW platelet distribution width, PCT procalcitonin, SII systemic immune-inflammation index, NLR neutrophil to lymphocyte ratio, PLR platelet to lymphocyte ratio, LMR lymphocyte to monocyte ratio, NPR neutrophil to platelet ratio, SIRI systemic inflammatory response index, PAR platelet to albumin ratio, CAR C-reactive protein to albumin ratio, CLR C-reactive protein to lymphocyte ratio, TyG index triglyceride-glucose index

Table 2
Comparison between SAP and N-SAP groups Abbreviations: SAP severe acute pancreatitis, AP acute pancreatitis, No. Pts number of patients, CRP C-reactive protein, RDW red blood cell distribution width, PDW platelet distribution width, PCT procalcitonin, SII systemic immune-inflammation index, NLR neutrophil to lymphocyte ratio, PLR platelet to lymphocyte ratio, LMR lymphocyte to monocyte ratio, NPR neutrophil to platelet ratio, SIRI systemic inflammatory response index, PAR platelet to albumin ratio, CAR C-reactive protein to albumin ratio, CLR C-reactive protein to lymphocyte ratio, TyG index triglyceride-glucose index

Table 3
Univariate logistic regression analysis for predictors of SAP Abbreviations: SAP severe acute pancreatitis, OR odds ratio, CI confidence interval, CRP C-reactive protein, RDW red blood cell distribution width, PDW platelet distribution width, PCT procalcitonin, SII systemic immune-inflammation index, NLR neutrophil to lymphocyte ratio, PLR platelet to lymphocyte ratio, LMR lymphocyte to monocyte ratio, NPR neutrophil to platelet ratio, SIRI systemic inflammatory response index, PAR platelet to albumin ratio, CAR C-reactive protein to albumin ratio, CLR C-reactive protein to lymphocyte ratio, TyG index triglyceride-glucose index

Table 4
Multivariate logistic regression models for identifying SAPAbbreviations: SAP severe acute pancreatitis, PCT procalcitonin, LMR lymphocyte to monocyte ratio, SII systemic immune-inflammation index, NLR neutrophil to lymphocyte ratio, CLR C-reactive protein to lymphocyte ratio, CI confidence interval, AUROC area under the receiver operating characteristic curve